M S Gilthorpe1, S J Cunningham. 1. Biostatistics Unit, Eastman Dental Institute for Oral Health Care Sciences, University College London, UK. m.gilthorpe@eastman.ucl.ac.uk
Abstract
OBJECTIVE: To demonstrate the use of multilevel multivariate modelling in the evaluation of multiple outcome dental data. BASIC RESEARCH DESIGN: Multiple outcome dental research data are used to illustrate the problems of analysing such complex information structures i.e. several outcomes clustered within subjects. Appropriate and statistically efficient methods of data analysis are proposed and illustrated step-by-step. The data structure is analysed using multilevel multivariate regression techniques and this process is discussed in comparison to conventional single-level multiple regression. PARTICIPANTS: Questionnaire data were obtained from an orthognathic study of 84 subjects seeking treatment and 106 'non-treatment' controls (full details of which are reported elsewhere). RESULTS: Multivariate multiple regression analysis demonstrated a number of advantages over separate single-level multiple regression approaches, including a gain in statistical efficiency and greater insight into: a) the role of (significant) explanatory variables and b) outcome variable interactions. Multilevel multivariate analysis reduced the risk of both Tipe I and Type II statistical errors. CONCLUSIONS: The study demonstrates the benefit of multilevel multivariate modelling over conventional single-level techniques for statistical analysis of multiple outcome data. As a result of ongoing technical developments in the power, speed and memory of modern PCs, multilevel multivariate regression can now be undertaken with relative ease. Consequently, researchers are better equipped to analyse such complex data structures, particularly within dentistry where multivariate data are common.
OBJECTIVE: To demonstrate the use of multilevel multivariate modelling in the evaluation of multiple outcome dental data. BASIC RESEARCH DESIGN: Multiple outcome dental research data are used to illustrate the problems of analysing such complex information structures i.e. several outcomes clustered within subjects. Appropriate and statistically efficient methods of data analysis are proposed and illustrated step-by-step. The data structure is analysed using multilevel multivariate regression techniques and this process is discussed in comparison to conventional single-level multiple regression. PARTICIPANTS: Questionnaire data were obtained from an orthognathic study of 84 subjects seeking treatment and 106 'non-treatment' controls (full details of which are reported elsewhere). RESULTS: Multivariate multiple regression analysis demonstrated a number of advantages over separate single-level multiple regression approaches, including a gain in statistical efficiency and greater insight into: a) the role of (significant) explanatory variables and b) outcome variable interactions. Multilevel multivariate analysis reduced the risk of both Tipe I and Type II statistical errors. CONCLUSIONS: The study demonstrates the benefit of multilevel multivariate modelling over conventional single-level techniques for statistical analysis of multiple outcome data. As a result of ongoing technical developments in the power, speed and memory of modern PCs, multilevel multivariate regression can now be undertaken with relative ease. Consequently, researchers are better equipped to analyse such complex data structures, particularly within dentistry where multivariate data are common.